visualization_plots

# Import data
restroom_cleaned = read_csv(here::here("./Data/Public_Restrooms_20241203.csv")) %>% 
  janitor::clean_names()

## Rows: 1047 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (12): Facility Name, Location Type, Operator, Status, Open, Hours of Ope...
## dbl  (2): Latitude, Longitude
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

subway_df = read_csv(here::here("./Data/NYC_Transit_Subway_Entrance_And_Exit_Data.csv")) %>% 
  janitor::clean_names()

## Rows: 1868 Columns: 32
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (22): Division, Line, Station Name, Route1, Route2, Route3, Route4, Rout...
## dbl  (8): Station Latitude, Station Longitude, Route8, Route9, Route10, Rout...
## lgl  (2): ADA, Free Crossover
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Clean restroom data
restroom_cleaned = restroom_cleaned %>% 
  select(
    -website, -operator, -hours_of_operation 
  ) %>% 
  rename(
    restroom_latitude = latitude,
    restroom_longitude = longitude,
    restroom_location = location
  ) %>% 
  mutate(
    restroom_latitude = as.numeric(restroom_latitude),
    restroom_longitude = as.numeric(restroom_longitude),
    restroom_location = st_as_sfc(restroom_location), #convert to point
    open = factor(
      open,
      levels = c("Future", "Seasonal", "Year Round"),
      ordered = TRUE
    ),
    accessibility = factor(
      accessibility,
      levels = c("Not Accessible", "Partially Accessible", "Fully Accessible"),
      ordered = TRUE
    ),
    changing_stations = case_when(
      changing_stations %in% c("Yes, in single-stall all gender restroom only",
                                "Yes, in women's restroom only",
                                "Yes") ~ 1,
      changing_stations == "No" ~ 0
    ),
    status = case_when(
      status %in% c("Not Operational",
                    "Closed for Construction",
                    "Closed") ~ 0,
      status == "Operational" ~ 1
    )
  ) 

# Convert dataframe to sf for spatial operations
restroom_sf = st_sf(restroom_cleaned, crs = 4326)

restroom_df = restroom_cleaned %>% 
  filter(location_type == 'Transit')

# Load the dataset
subway_df = read_csv(here::here("./Data/NYC_Transit_Subway_Entrance_And_Exit_Data.csv"))

## Rows: 1868 Columns: 32
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (22): Division, Line, Station Name, Route1, Route2, Route3, Route4, Rout...
## dbl  (8): Station Latitude, Station Longitude, Route8, Route9, Route10, Rout...
## lgl  (2): ADA, Free Crossover
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Clean the dataset
subway_cleaned = subway_df %>% 
  janitor::clean_names() %>% 
  select(
    line, station_name, station_latitude, station_longitude, 
    route1, route2, route3, route4, route5, route6, 
    route7, route8, route9, route10, route11, 
    entry, exit_only, vending, entrance_type, ada
  ) %>%  
  mutate(across(starts_with("route"), ~ replace_na(as.character(.), ""))) %>% 
   mutate(
    station_latitude = as.numeric(station_latitude),
    station_longitude = as.numeric(station_longitude)
  )

NYC Public Restrooms

Type of Restroom Location

restroom_location_bar = restroom_cleaned |> 
  group_by(location_type) |> 
  count(location_type) |> 
  mutate(loation_type = fct_reorder(location_type, n)) |> 
  plot_ly (x = ~location_type, y = ~n, color = ~location_type,
           type = "bar",text = ~n, textposition = "outside") |> 
  layout(
    xaxis = list(title = "Location Type"),   
    yaxis = list(title = "Number of Restrooms") 
  )

 restroom_location_bar

The majority of collected restrooms are located in Park and Library, and only a few restrooms are located in Privately Owned Public Space, Public Plaze, and Transit. This might provide guidance for the future establishment of public restrooms: public plaza have siginificant foot traffic, yet public restrooms are so scarce.

Whether or not the Restroom is Only Operational on a Seasonal Schedule

restroom_open_bar = restroom_cleaned |> 
  filter(!is.na(open))|>
  group_by(open) |> 
  count(open, name = "n") |> 
  ungroup()|>
  mutate(open = fct_reorder(open, n)) |> 
  plot_ly (x = ~open, y = ~n, color = ~open,
           type = "bar", text = ~n, textposition = "outside",
           texttemplate = "%{text}") |> 
  layout(
    xaxis = list(title = "Open Type"),   
    yaxis = list(title = "Number of Restrooms") 
  )

 restroom_open_bar

From the collected data, New York City provides public restroom facilities as follows, 864 open Year Round, 123 open Seasonal, and 12 open Future. Most public restrooms are open year-round, which is good news!

Are Location Type and Open time related?

location_type_open <- 
  restroom_cleaned |> 
  filter(!is.na(open)) |> 
  filter(!is.na(location_type))|>
  group_by(location_type, open) |> 
  summarise(count = n(),.groups="drop") |> 
  group_by(open)|>
  mutate(percent = round(count / sum(count) * 100, 1))
 

location_type_open |>
  mutate(hover_text = str_c("Open: ", open, 
                            "<br>Location Type: ", location_type, 
                            "<br>Percent: ", percent, "%")) |> 
  plot_ly(
    x = ~open, 
    y = ~percent, 
    color = ~location_type,
    type = "bar", 
    colors = "viridis", 
    text = ~hover_text, 
    hoverinfo = "text"
  )|>
  layout(
    xaxis = list(title = "Open"),   
    yaxis = list(title = "Percent of Restroom (%)"))

From the plot, we can get that the open type is related to location type. All future open-status public restrooms are located in library. All seasonal open-status public restrooms are located in park. This might be due to the impact of climate on the park and its visitor numbers.

The Level of ADA Accessibility where the Restroom is Located

restroom_accessibility_bar = restroom_cleaned |> 
  filter(!is.na(accessibility))|>
  group_by(accessibility) |> 
  count(accessibility, name = "n") |> 
  ungroup()|>
  mutate(accessibility = fct_reorder(accessibility, n)) |> 
  plot_ly (x = ~accessibility, y = ~n, color = ~accessibility,
           type = "bar",text = ~n, textposition = "outside") |> 
  layout(
    xaxis = list(title = "Accessibility Type"),   
    yaxis = list(title = "Number of Restrooms") 
  )

 restroom_accessibility_bar

Although the number of Fully Accessible is the highest, there are still quite a few that are Not Accessible. We still need to work on improving the implementation of public restrooms to provide services for everyone.

The Number and Gender Designation of the Stalls

restroom_type_bar = restroom_cleaned |> 
  filter(!is.na(restroom_type))|>
  group_by(restroom_type) |> 
  count(restroom_type, name = "n") |> 
  ungroup()|>
  mutate(restroom_type = fct_reorder(restroom_type, n)) |> 
  plot_ly (x = ~restroom_type, y = ~n, color = ~restroom_type,
           type = "bar", text = ~n, textposition = "outside") |> 
  layout(
    xaxis = list(title = "Restroom Type"),   
    yaxis = list(title = "Number of Restrooms")
  )

 restroom_type_bar

Multi-Stall W/M Restrooms are the most commonly built, while Multi-Stall All Gender Restrooms are the least, with only 4. We need to provide more Multi-Stall All Gender Restrooms so they can serve all groups and more people at same time.

NYC Subway

Vending machine status

subway_vending_pie = subway_cleaned |> 
  group_by(vending) |> 
  count(vending, name = "n") |> 
  mutate(vending = fct_reorder(vending, n)) |> 
  plot_ly (labels = ~vending, values = ~n, type = "pie",
           textinfo = "label+percent", 
           marker = list(colors = viridis::viridis(2)) )|> 
  layout(title = "Subway Vending"
     )

 subway_vending_pie

The pie chart tells that mostly subway stations have vending machines now.

Entrance Type of Subway Station

subway_entrance_bar = subway_cleaned |> 
  group_by(entrance_type) |> 
  count(entrance_type, name = "n") |> 
  mutate(entrance_type = fct_reorder(entrance_type, n)) |> 
  plot_ly (x = ~entrance_type, y = ~n, color = ~entrance_type, colors = "viridis", text = ~n, textposition = "outside", type = "bar") |> 
  layout(
    xaxis = list(title = "Subway Entrance Type"),   
    yaxis = list(title = "Number of Subway Stations") 
  )

 subway_entrance_bar

Majority of subway stations’ entrance type is Stair. The least common subway station entrance is Walkway and Ramp.

Are Entrance Type and ADA level for Subway Station related?

entrance_type_ada <- 
  subway_cleaned |> 
  group_by(entrance_type, ada) |> 
  summarise(count = n(),.groups="drop") |> 
  group_by(entrance_type)|>
  mutate(percent = round(count / sum(count) * 100, 1))

entrance_type_ada |>
  mutate(hover_text = str_c("Entrance Type: ", entrance_type, 
                            "<br>Ada: ", ada, 
                            "<br>Percent: ", percent, "%")) |> 
  plot_ly(
    x = ~entrance_type, 
    y = ~percent, 
    color = ~ada,
    type = "bar", 
    colors = "viridis", 
    text = ~hover_text, 
    hoverinfo = "text"
  )|>
  layout(
    xaxis = list(title = "Entrance Type"),   
    yaxis = list(title = "Percent of Subway Stations (%)"),
    title = "")

This plot shows the relationship between entrance type of subway stations and their ADA level. The stations with walkway or elevator entrances are more likely to be considered true in ADA level, while stations with door, ramp, or stair entrances are more likely to be considered false in ADA level. However, according to previous plot, we really need to build more Walkway, Easement, Elevator and Escalator entrances for the subway stations.